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Why is this important? The next slide contains employment multipliers for selected industries Computed from REMI Standard National Control Assumes 1% change in employment in sector listed Computed as: Change in total employment divided by Change in employment in sector listed Example (Construction Sector): 921.922/437.519 = 2.11 Where 921.922 = difference from REMI baseline in total employment 437.519 = 1% difference from REMI baseline in Construction Employment

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Selected Industry Total Employment Multipliers: REMI Standard National Control The employment multipliers represent the estimated impact of an additional job in the sector listed on total employment Source: Author calculations using REMI PI+ standard national control. Multipliers are for 2010.

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Percent Change in REMI Oil and Gas Extraction Employment Standard National Control

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A National (and State) REMI Experiment: and some implications for New Mexico Step 1: Create new national control – Impose the variability (% change) in oil and gas employment observed from 2000 to 2009 on projected years 2010 to 2019. Step 2: Run regional model using new national control – With no other changes to the regional model

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A National (and State) REMI Experiment: Step 1 Create New National Control Can also be done through labor demand block

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A National (and State) REMI Experiment: Pros and Cons Advantages of this technique: – It is easy –very easy– to implement – It does capture historic variability that is not captured in the standard controls and, it makes a difference in the results Disadvantages – Timing –who knows when the variability will occur?

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A Second National (and State) REMI Experiment: Also Easy Use μ (mean) and σ (standard deviation) of previous decade variability – Or some other plausible μ and σ Draw random numbers from this distribution to obtain inputs – Assume the distribution is normal – or some other distribution since the world is not normal Construct new national control Examine impacts at state level – No other changes at state level

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A Second National (and State) REMI Experiment: Inputs used in EMPL in oil and gas extraction sector Compared to implied historic

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A Second National (and State) REMI Experiment: Results: % Chg from Standard Regional Control Oil and Gas Extraction Employment and Other Sectors

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A Monte Carlo National (and State) REMI Experiment : Fun, but dont try this one …. Use μ (mean) and σ (standard deviation) of previous decade variability – Or some other plausible μ and σ Draw 100 (250?) random samples (see next slide) – from normal or some other distribution Run the previous experiment in REMI using each sample Save the results Construct interval estimates – Many ways to do this

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Who are these guys? They are Arrowhead slaves who did the 250 REMI runs. Mikidadu Mohammed, Graduate Student Leo Delgado, Policy Analyst

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A Monte Carlo National (and State) REMI Experiment: What the spreadsheet might look like

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A Monte Carlo National (and State) REMI Experiment: Some Interval Estimates Ranges shown are in percentage differences in New Mexico Oil and Gas Extraction employment. The 12 highest and 12 lowest observations were removed.

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A Monte Carlo National (and State) REMI Experiment: Some More Interval Estimates Ranges shown are percentage differences in New Mexico Oil and Gas Extraction employment. These are the maximum and minimum ranges from 250 trials. Notice the change In scale from the Previous chart

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A Monte Carlo National (and State) REMI Experiment: Advantages and Disadvantages Advantages – Explicit recognition that: Energy Market Variability (EMV) is likely The timing (years) is unknown and unknowable – Produces a plausible(?) range of estimates rather than a point estimate – Satisfies ceremonial requirements of complexity and sophistication Disadvantages – The intervals are far too large to be meaningful? – Not necessary due to linearity of REMI responses – Time consuming (expensive) – Distributional assumption or The World is Not Normal – Stationarity of μ and σ – Other less time consuming methods to generate intervals – The intervals do not get larger as t gets larger

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Some Conclusions Variability matters – even in a relatively short (10 year) time horizon – even when only oil and gas extraction is considered Variability here to stay? – More than likely Not difficult to impose some variability on the standard controls Key questions – How much variability? – When will it occur? Answers to key questions are unknown and unknowable – but this should not deter some attempt to impose variability – when stability (smoothness) seems rare